In recent years, there has been an increasing need for human-computer interfaces, or HCI's, for use in various control applications. A dominant underlying technology behind muscle computer interfaces is the sensing of muscle activity through the surface of the skin, called sEMG (surface electromyography), to sense hand and arm gestures a user is performing. For example, a device may be worn on the wrist or forearm of a user that contains EMG sensors for the acquisition of this electrical activity. Pattern recognition algorithms are used to analyze the electrical data to determine the gestures that the user is making. There is a comprehensive overview of this technology and its limitations in U.S. Pat. No. 8,170,656.
In order for these types of gesture sensing devices to be commercially viable, the devices must have a very high gesture detection rate with a relatively low build cost. One limitation is that sEMG based sensors are very susceptible to variations in operating conditions, and signals generated by sEMG sensors may be affected by such variables as skin perspiration, amount of hair, and fat content in the skin. Because of this, it is very difficult to achieve high gesture recognition rates using just sEMG sensors alone.
Therefore, what is needed is an effective HCI device which overcomes these limitations in the prior art.